What qualifies as a mobile device? Believe it or not, we had a brief – and surprisingly illuminating – debate at the OpenText Analytics office the other day that started with this question.

Let me provide some context: Our team is gearing up to produce technology demonstration projects and other content that connects Big Data analytics to mobile devices. (Both literally and figuratively – more on that in a bit.) Some of this content is slated for a webinar scheduled for July 9; also, mobility was an umbrella theme for our corporate blogs for the month of July. (I’ve linked a few of the other mobile-related pieces at the end of this post.)

But others worried that focusing too much attention on wearables might exclude the more traditional devices, such as smartphones and tablets, that are critical to our community today. One individual added that most smartwatch functionality still requires a tethered phone, and most fitness trackers must sync to a computer, meaning wearables are an adjunct to mobility rather than devices unto themselves.

After more discussion, we decided to keep the definition of mobile devices broad, while focusing some specific time and attention – particularly in the webinar – on the wearables market.

The Mobility-Data Science Link

You may wonder how this debate relates to data science. There’s one overarching parallel that I see: In both worlds – mobility and data science – we as technologists and practitioners need to keep our eye on the future while simultaneously staying relevant to what real people actually do every day. We can and should debate which nascent concepts and technologies will become commonplace in the future, but we also need to understand and address the challenges that our users face today and the tools they use to address those challenges.

There’s another, more direct connection between data science and mobility: With increasing frequency, data science provides the critical link between Big Data – in all its volume, variety, and velocity – and data that is usable and actionable on a mobile screen. (This is what our Allen Bonde likes to call “Small Data.”) Data science, in this context, is not entirely about algorithms and ideas; it also relates to the tools and techniques we use to make those connections happen.

Here are some important factors I have identified in the connection between data science and mobility:

APIs: Increasing diversity of mobile devices – and their decreasing size – highlights the ongoing need for, and importance of, APIs. Ideally, APIs used by Big Data systems must have enough flexibility that they can address devices not yet invented.

Two-Way: Mobile devices aren’t just receivers of data; they’re also generators of data. Any Big Data system related to mobile devices – and wearables in particular – ignores this truth at its peril.

Security: A mobile device is a potential security weak link. (Actually, both the device itself and the network connection between the device and its supporting analytics systems are potential points of exploit.) Addressing this truth is vital when mobile devices are linked to Big Data systems containing valuable or secret information.

What do you see as the connections, both conceptual and practical, between data science and mobility? Countless interesting projects are out there, in areas ranging from medicine to logistics to marketing to meteorology. I’d love to know what everybody is working on.